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enumerate_devices

Lists all connected devices on the system for mobile and desktop application analysis with Frida MCP, providing device IDs, names, types, and connection details.

Instructions

List all devices connected to the system.

Returns:
    A list of device information dictionaries containing:
    - id: Device ID
    - name: Device name
    - type: Device type
    - hint: How to reference the device via device_id
    - alias: Configured alias for remote devices (if any)
    - default_candidate: Whether the device is the current default choice

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • MCP tool handler for 'enumerate_devices', decorated with @mcp.tool(). Returns formatted device list via describe_devices() helper. Includes schema in type annotation and docstring describing output fields.
    @mcp.tool()
    def enumerate_devices() -> List[Dict[str, Any]]:
        """List all devices connected to the system.
    
        Returns:
            A list of device information dictionaries containing:
            - id: Device ID
            - name: Device name
            - type: Device type
            - hint: How to reference the device via device_id
            - alias: Configured alias for remote devices (if any)
            - default_candidate: Whether the device is the current default choice
        """
        return describe_devices()
  • Thin wrapper around DeviceSelector.describe_devices(), ensuring selector is initialized.
    def describe_devices() -> List[Dict[str, str]]:
        global _selector
        if _selector is None:
            _selector = DeviceSelector()
        return _selector.describe_devices()
  • Core implementation: Enumerates devices with frida.enumerate_devices(), adds metadata like hints, aliases, and default status using config.
    def describe_devices(self) -> List[Dict[str, str]]:
        devices = self._frida.enumerate_devices()
        descriptions: List[Dict[str, str]] = []
        default_choice = (
            self._config.default_device or "auto"
        ).strip().lower() or "auto"
        fallback_order = self._config.fallback_order()
    
        for device in devices:
            entry = {
                "id": device.id,
                "name": device.name,
                "type": device.type,
            }
            entry["hint"] = self._usage_hint(device)
            entry["default_candidate"] = self._is_default_candidate(
                device, default_choice, fallback_order
            )
            alias = self._config.address_to_alias.get(
                _normalize_remote_identifier(device.id)
            )
            if alias:
                entry["alias"] = alias
            descriptions.append(entry)
        return descriptions
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the return format and structure, which is valuable, but doesn't cover other behavioral aspects like performance characteristics, error conditions, or whether the list is real-time vs. cached. It's adequate but has clear gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded: the first sentence states the purpose clearly, followed by a detailed breakdown of the return format. Every sentence adds value without redundancy, making it efficient and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (0 parameters, no annotations, but with an output schema implied by the return description), the description is reasonably complete. It explains what the tool does and what it returns in detail. However, it could improve by addressing usage context relative to siblings, slightly reducing completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and the input schema has 100% coverage (though empty). The description appropriately doesn't discuss parameters, focusing instead on the output. This meets expectations for a parameterless tool, earning a high baseline score.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('List') and resource ('all devices connected to the system'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_device' or 'get_local_device', which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'get_device' or 'get_local_device'. It lacks context about use cases, prerequisites, or exclusions, leaving the agent to infer usage from the purpose alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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